Interoperability mechanisms for internet of things integration platform

ABSTRACT

A method of maintaining interoperability amongst Internet of Things (IoT) devices connected via an IoT integration platform is disclosed. The method includes: receiving a selection of a semantic label associated with an IoT device; determining a recommendation of an interoperable rule based on the semantic label, the interoperable rule having a condition trigger and an action policy for execution at satisfaction of the condition trigger; presenting the recommendation on a rule management interface; and receiving a confirmation from a user through the rule management interface to activate the interoperable rule.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/957,255 filed Aug. 1, 2013, which claims the benefits of U.S.provisional patent application Ser. No. 61/845,369 filed Jul. 11, 2013,and the subject matter thereof is incorporated herein by reference inits entirety.

FIELD OF INVENTION

This invention relates generally to the technical areas of the Internetof Things.

INTRODUCTION

The Internet of Things (IoT) refers to uniquely identifiable devices andtheir virtual representations in an Internet-like structure. The conceptof IoT devices includes networked devices (“connected devices”) capableof communicating with a server or with a mobile application via anetwork connection other devices (“connected device”). The networkeddevices may include passive and active devices, where the passivedevices may achieve network connectivity through interactions with theactive devices. IoT devices are intended to achieve ubiquitousconnection for intelligent perception, identification and management ofitems and processes. Many considers IoT as the third wave of developmentof information industry following the computer and the Internet.However, solutions for management of IoT devices are generally verticalsolutions.

DISCLOSURE OF TECHNOLOGY

Disclosed is a technology for creating an integration platform for theInternet of Things (“the technology”). The technology further enhancesthe integration platform to connect to not only devices, but also otherphysical entities, such as places and people (“Internet of Everything”).The technology is a consumer solution to consolidate and automate users'connected environment. The technology can identify and profile connecteddevices around a consumer, communicate with the connected devices, andcreate logical connections amongst people, devices, locations, digitalchannels, or any combination thereof.

The technology may be implemented by the integration platform. Theintegration platform may include a consolidation interface, a datacorrelation module, a data analysis module, and a rule developmentmodule. The consolidation interface is a centralized interfaceaccessible via one or more of the networked devices. The consolidationinterface may include a rule creation interface. The consolidationinterface may include an interactive visual component including aninteractive user interface and visual recognition of places, situations,and people, an interactive audio component including voice control, aninteractive gesture component, or any combination thereof. Theconsolidation interface provides a single interface to view/editconsolidated data and to interact with the networked devices, such asvia the rule creation interface. The data correlation module associatesdata and metadata from the networked devices to relate these data and/ormetadata to a user. The data analysis module analyzes the collected dataand metadata to determine specific semantic label or context relevant tothe user. The rule management module enables configuration, adjustments,and interactions with the networked devices based on user-profile,context, event trigger, user behavior, social interactions, userconfigurations, or any combination thereof.

The rule management module may embody these configurations, adjustments,and interactions in one or more interoperable rules. These interoperablerule may be executed on the connected devices. The interoperable rulesmay be implemented in reference to any node, such as any person, place,device, group, or other entity, thing or object. Because of contextrecognition as enabled by the data analysis module, the one or moreinteroperable rules for each node may be designed and manipulated incontext.

Some embodiments of the invention have other aspects, elements,features, and steps in addition to or in place of what is describedabove. These potential additions and replacements are describedthroughout the rest of the specification

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating vertical solutions provided forconnected devices.

FIG. 1B is a block diagram illustrating an integration platformoperating in conjunction with the vertical solutions.

FIG. 2 is a block diagram illustrating an example system environment ofa internet of things (IoT) integration platform system.

FIG. 3 is a block diagram illustrating the IoT integration platformsystem.

FIG. 4 is an example of an user interface illustrating an activelifeline diagram for a user account on an integration interface,consistent with various embodiments of the disclosed technology

FIG. 5 is a diagrammatic representation of a machine in the example formof a computer system within which a set of instructions, for causing themachine to perform any one or more of the methodologies or modulesdiscussed herein, may be executed.

FIG. 6 is a diagrammatic representation of a wireless device.

FIG. 7 is a flow diagram of a method of data consolidation, consistentwith various embodiments of the disclosed technology.

FIG. 8 is a flow diagram of a method of interoperable IoT rulemanagement, consistent with various embodiments of the disclosedtechnology.

FIG. 9A is an exemplary screen shot illustrating a rule managementinterface of an integration platform at a rule activation stage,consistent with various embodiments of the disclosed technology.

FIG. 9B is an exemplary screen shot illustrating the rule managementinterface of the integration platform at a condition selection stage,consistent with various embodiments of the disclosed technology.

FIG. 9C is an exemplary screen shot illustrating the rule managementinterface of the integration at an action selection stage, consistentwith various embodiments of the disclosed technology.

FIG. 10A is an exemplary screen shot illustrating the consolidationinterface showing a correlative insight, consistent with variousembodiments of the disclosed technology.

FIG. 10B is an exemplary screen shot illustrating the consolidationinterface generating an interoperable rule from the correlative insight,consistent with various embodiments of the disclosed technology.

FIG. 11A is an exemplary screen shot illustrating a semantic camerainterface at a first stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology.

FIG. 11B is an exemplary screen shot illustrating the semantic camerainterface at a second stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology.

FIG. 11C is an exemplary screen shot illustrating the semantic camerainterface at a third stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology.

FIG. 11D is an exemplary screen shot illustrating the semantic camerainterface at a fourth stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology.

The figures depict various embodiments of the technology for purposes ofillustration only. One skilled in the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the technology described herein.

DETAILED DESCRIPTION Integration

Every day more and more newly connected devices are introduced into themarket each offering a vertical solution with a specific functionality.All these various solutions do not communicate to each other. Forexample, FIG. 1A is a block diagram illustrating vertical solutionsprovided for connected devices, such as device-A 102A, device-B 102B,and device C 102C (collectively “devices 102”). Each connected devicesservice provider may have its own vertical solution with a clientinterface (e.g., mobile or web), such as interface-A 104A andinterface-B 104B (collectively “interfaces 104”), and one or more cloudservices for computation, such as cloud service-A 106A or cloudservice-B 106B (collectively “cloud services 106”).

Different devices 102 use different communications protocols made bydifferent manufacturers. The integration platform may act as singlepoint of interaction enabling cross-device and cross technologycommunication. FIG. 1B is a block diagram illustrating an integrationplatform operating in conjunction with the vertical solutions. Theintegration platform may be implemented through communicationintegration, including an unified application programming interface(API), unified software development kit (SDK), unified protocol(s),and/or interoperability interfaces between different connected devices.The integration platform mainly can be implemented by an integrationservice system 112 (e.g., the computer system 500 of FIG. 5) and anintegration interface 114 (e.g., mobile or web). The integration servicesystem 112 may provide different services for integration of the IoTdevices 102 and for providing an execution environment for applicationsrelated to the use of the IoT devices 102. The integration interface 114may be a software application that runs on a local computing device thatis capable of managing or integrating the IoT devices 102 within a localnetwork.

System Environment

FIG. 2 is a block diagram illustrating an example system environment ofan internet of things (IoT) integration platform system 200. The IoTintegration platform system 200 includes IoT devices 202, such as theIoT devices 102 of FIG. 1. IoT devices 202, for example, can be smartphones, smart watches, smart sensors (e.g., mechanical, thermal,electrical, magnetic, etc.), networked appliances, networked peripheraldevices, networked lighting system, communication devices, networkedvehicle accessories, smart accessories, tablets, smart TV, computers,smart security system, smart home system, other devices for monitoringor interacting with or for people and/or places, or any combinationthereof. The IoT devices 202 may include the wireless device 600 of FIG.6. The IoT devices 202 may include one or more of the followingcomponents: sensor, radio frequency identification (RFID) technology,global positioning system technology, mechanisms for real-timeacquisition of data, passive or interactive interface, mechanisms ofoutputting and/or inputting sound, light, heat, electricity, mechanicalforce, chemical presence, biological presence, location, time, identity,other information, or any combination thereof.

The IoT device 202 are connected via a network 204. The network 204 mayinclude different channels of communication and may include localnetworks therein. For example, the network 204 may include wirelesscommunication through cellular networks, WiFi, BlueTooth, Zigbee, or anycombination thereof. The network 204 may include one or more switchesand/or routers, including wireless routers that connect the wirelesscommunication channels with other wired networks (e.g., the Internet). Alocal network may exist that connects a local set of the IoT device 202.For example, the local network may be established by a local router or alocal switch.

For example, the IoT device 202 may be connected to a control device 206within the local network. The control device 206 may be a computer, anetworked device, or a mobile device, such as the wireless device 600.The control device 206 may include an interface implemented by asolution-specific application 208. An IoT device may be coupled to thesolution-specific application 208, where the solution-specificapplication 208 is created specifically to communicate with such devicesas part of a vertical solution. The solution-specific application 208may be able to control the IoT device or to access data from the IoTdevice.

IoT devices may also communicate with a solution-specific service serversystem 210. For example, a networked lighting system may communicatewith the solution-specific service system 210 keeping track of whetherlights are on/off on the solution-specific service system 210. Thesolution-specific service system 210 can create an interface to sharethat data and/or to interact with the IoT devices. The interface may beaccessible through the solution-specific application 208 or through abrowser.

The technology disclosed includes an integration service system 212,such as the integration service system 112, and an integration interface214, such as the integration interface 114, that can manage or integratemultiple instances of the IoT devices 202 and co-exist with the verticalsolutions. The integration interface 214 may run on the control device206. The integration interface 214 may be a computing application, a webpage, other interactive interface operable on a computing device. Thedisclosed integration service system 212 and/or the integrationinterface 214 overcome the challenge of user inconvenience. Vendors whomanufacture IoT devices 202 do not have a consistent standard to providea unified interface. The proposed system provide a technology includingspecific modules and processes to coordinate with the multiple IoTdevices 202, the multiple solution-specific applications 208, and themultiple solution-specific service server systems 210.

The integration service system 212 may include a profile store 214 ofIoT devices 202 and other context relevant entities. For example,context relevant entities may include people, places, groups, physicalobjects, brands, things, or any combination thereof. Some of the contextrelevant entities in the integration service system 212 may not includeany networking capabilities, but may be observed through the connectedIoT devices 202 either directly or indirectly. The integration servicesystem 212 may profile these entities, such as via a data analysismodule 308 described below in FIG. 3, and store these entity profiles inthe profile store 214 for reference. Interaction with these profiledentities may be enabled by visual identification (e.g., imagerecognition), voice recognition, motion detection, geo-location, otherinput data to the IoT devices 202, or any combination thereof.

As an example, an IoT device 202 with a camera can recognize a knownuser is in front of a plate and fork (e.g., within a context of“eating”). Such recognition may trigger an increase of the known user'starget daily steps in his/her activity monitor (e.g., due to highercalorie intake), or trigger the known user's coffee machine to preparean espresso. In this example the plate and fork do not have networkcapability, but do have profiles in the profile store 214. Therecognition of the profiles may be a trigger for an action in theconnected devices.

As another example, detecting proximity to a person (e.g., a child) or aplace (e.g., a child's room) may trigger wearable IoT devices 202 (e.g.,sensors) on the child to synchronize relevant data to the integrationservice system 212, such as the child's glucose level. The integrationservice system 212 may respond to this trigger by analyzing the relevantdata, and visualizing a relevant correlative insight (e.g., “inject moreinsulin”) based on the analyzed data on a parent's smartphone orwearable device. The integration service system 212 may further executean interoperable rule upon detection of the trigger, such as by sendinga command to another IoT device (e.g., an insulin pump). In thisexample, the trigger may be based on a contextual or semantic profilingof a person or place, and not necessarily another device.

System Architecture

FIG. 3 is a block diagram illustrating the IoT integration platformsystem 300. The modules therein may be implemented by a computer system,such as the computer system 500 of FIG. 5. The IoT integration platformsystem 300 may include an integration backend system 302, such as theintegration service system 212 of FIG. 2. The integration backend system302 may include a consolidation interface generator 304, a datacorrelation module 306, a data analysis module 308, a rule generationmodule 310, a device identification module 312, a rule execution module314, and an event track module 316. The integration backend system 302may also include a communication interface module 318 for interfacingwith the IoT devices and/or client interfaces.

The IoT integration platform system 300 may enable integration ofvertical solutions of IoT devices, such as the vertical solutionsillustrated in FIGS. 1A and FIG. 1B. For example, a vertical solutionmay include a solution-specific backend system 320, a solution-specificapplication 322, and/or a first IoT device 324A. For example, the firstIoT device 324A may communicate with the solution-specific backendsystem 320 and/or the solution-specific application 322 as part of thevertical solution. The IoT integration platform system 300 enables asecond IoT device 324B to become connected, even though the second IoTdevice 324B is not part of the vertical solution. Collectively, theconnected devices including the first and second IoT devices 324A and324B may be referred to as “the IoT devices 324.” The technology toimplement such integration may be achieved via the integration backendsystem 302, an integration application 328, such as the integrationinterface 214 of FIG. 2, or both. In various embodiments, any one ormore of the modules of the integration backend system 302 may beimplemented within the integrated application 328.

For example, integration may be achieved in any combination of thefollowing methods, including cloud-based integration, mobile-basedintegration, and device-based integration. The integration method maydepend on the manufacture of each of the IoT devices 324. Regardless ofthe integration method, the integration backend system 302 and/or theintegration application 328 are informed of the existence of any newlyconnected IoT device.

For cloud-based integration, the communication interface module 318enables communication between or amongst the integration backend system302 and one or more of the solution specific backend systems 320. Formobile-based integration, the integration application 328 maycommunicate with the solution-specific application 322. For example,this communication may be achieved by providing an integration SDK 326to the solution-specific application 322 and/or the integrationapplication 328. For device-based integration, the integrationapplication 328 may communicate with the IoT devices 324 belonging todifferent vertical solutions via an open protocol. For example, theintegration application 328 may scan different local networks (e.g.,Wi-Fi, Bluetooth, BlueTooth Low Energy, Zigbee, etc.), identify the IoTdevices 324, and connect to control interfaces of each of the IoTdevices 324.

For example, the communication interface module 318 may include an agentfor communicating with an API on the solution-specific backend system320. Alternatively, the communication interface module 318 may includean API of its own, and may allow the solution-specific backend system322 to send/retrieve data, including real-time data, context data,sensor data, sensor metadata, configuration data, or any combinationthereof, through the API.

As another example, the integration SDK 326 can embed communicationprocesses/protocols for communicating with the communication interfacemodule 318 of the integration backend system 302 and/or the integratedapplication 328. In some embodiments, the integrated application 328 maybe distributed from the integration backend system 302. For example, theIoT devices 324 that have Wi-Fi connectivity may become visible to theintegration platform 300 when in the Wi-Fi networks where theintegration application 328 is connected.

As yet another specific example, an IoT device 324, such as the secondIoT device 324B may integrate directly with the integration backendsystem 302 and/or the integrated application 328. The second IoT device324B may be configured such that the protocols and processes forcommunicating with the integration backend system 302 and/or theintegrated application 328 are stored thereon. Optionally, a deviceintegration SDK (not shown), similar to the integration SDK 326, may beconfigured within an IoT device 324. The integration device SDK mayenable the IoT devices 324 to communicate with the integration backendsystem 302, the integrated application 328, and/or with each other(shown as a dotted line connection).

Device Identification

The device identification module 312 is configured to generate a uniqueID for each IoT device detected by the integration platform 300. Theunique ID enables tracking of the IoT devices for the purposes ofauthentication, data access permissions and security, data correlation,data analysis, rule generation, rule execution, event tracking, and/oruser interface. In some embodiments, the device identification module312 may also detect the type and/or make of the IoT devices. The typemay define the data structure of the IoT device, actions that areapplicable to IoT device, and/or communication protocols and processesof the IoT device. As a specific example, correlating device features ofa smart home automation light switch enables the data consolidationmodules to communicate with the light switch and the interoperable rulemodules to control and manage the light switch. The deviceidentification module 312 can simplify the connectivity process ofadding a new device by identifying the new device completely orpartially without user input.

Identifying virtually and physically connected devices around a user ora place relevant to the user is an important component for theinteroperability function. For example, the device identification module312 may employ at least one of the following methods to identify the IoTdevices 324: (a) device based identification, where unique IoT deviceidentifier may be created based on the device's data elements; (b)protocol based identification, where unique device identifier may becreated based on a device communication element or elements; (c) deviceand protocol based identification, where a combination of device dataand communication protocol data may define the device identification;(d) device behavior based identification, where the device is identifiedbased on its predefined or observed behavior, or any combinationthereof. Behavior can be, for example, audible, visual, magnetic,electronic, motion, or any combination thereof. Behavior pattern can bepassive or responsive to a command or stimulus. The deviceidentification module 312 may recognize an IoT device behavior based onIoT device data, a test and verification command, or a context eventassociated with the IoT device. The test and verification command, forexample, may include the device identification module 312 sending ablinking command to a connected light bulb in order to identify theexact bulb of multiple bulbs around. In this manner, the recognition ofa context (e.g., behavioral context) and/or semantic circumstance of theIoT device (e.g., the light bulb) can define the unique identifier ofthe IoT device.

Device identification can be based on image recognition.Image-recognition-based device identification may be assisted byrelevant data including contextual parameters from the data correlationmodule 306, the data analysis module 308, or both. For example, if thedevice identification module 312 analyzes a device and determines thatthe device may be either be a Samsung™ fridge or a Samsung™ TV, thedevice identity may be determined based on its geo-location context(e.g., if the device is in living room, then the device is a TV, and ifthe device is in kitchen, then the device is a fridge). In this case,for example, location may be determined through radio frequency (RF)beacons of other devices around or through geo-locating components. Inthe example of RF beaconing, if a microwave and coffee machine aredetected around the device, then the device is probably in the kitchen.Location may further be assisted by semantic data. For example, if thedevice is proximate (e.g., within a distance threshold) to a bulb namedby the user as “kitchen light”, then the device is in the kitchen.Proximity may be estimated by distance through router signal strengthindication (RSSI) or visual recognition.

Data Consolidation

The task of data consolidation may be divided into steps. For example,data consolidation may be performed in the following steps including:(1) data extraction, (2) data aggregation, (3) data normalization, (4)data correlation, (5) data analysis, and (6) data visualization. Anexample of the data consolidation flow is illustrated in FIG. 7. Theintegration backend system 302 and/or the integration application 328may implement any combination of the steps for data consolidation. Forexample, when a user operates an activity tracker and a network scale,the integration platform 300 enables a consolidated interface to presenta correlative view of the user's weight and activities in a meaningfulmanner to assist the user in monitoring his/her health. The combinationof the above steps enables unique correlation features that have beendiscovered to be advantageous. The data consolidation steps enablecontextual and semantic insights based on a multitude ofsensor/measurement/semantic/analytical/user-reported/device status data,rather than just a listing of sensor data. For example, the dataconsolidation may provide the user with health related insights ofhimself (e.g., when the user goes to work using public transport, theuser burns four times more calories than when driving a car to work).Thus, the data consolidation steps may be used to motivate the user tochange habits and behavioral patterns that affect his/herfitness/health. The data correlation module 306 is configured to extractraw data from different sources, including the IoT devices 324. Forexample, the data correlation module 306 can receive data (e.g.,real-time from continuous or discrete data stream, non-real-time data,device sensor data, user-device interaction dataset, user reportingdataset, or any combination thereof including metadata thereof) from theIoT devices 324. For example, the data may include measurements, usercommands, or user-reported status updates. The data correlation module306 may receive the datasets directly from the IoT devices or viareporting from the solution-specific application 222 or the integratedapplication 328.

The data correlation module 306 may further be configured to extract rawdata from an external source. For example, the raw data may be extractedfrom an online or virtual data source, such as a geo-location map, asocial network, a calendar, a media network, or any combination thereof.

The data correlation module 306 may yet further be configured to extractdata based on data analysis, such as the data analysis performed by thedata analysis module 308. Data analysis may include semantic analysisand context awareness analysis. Further description of data generatedfrom the data analysis module 308 is described herein.

The data correlation module 306 may be configured to aggregate thedatasets into meaningful data buckets during the process of datacollection. As data is extracted, the data is organized into themeaningful buckets (e.g., cluster). The data aggregation may be based ona time line, based on user, based on device type, based on user-definedgroups, based on location, or any combination thereof.

The data correlation module 306 may be configured to normalize each ofthe datasets. For example, data along the same dimension may benormalized across time periods, across the data buckets throughaggregation, or a combination thereof.

The data correlation module 306 may also be configured to correlateportions of the datasets with each other. Data correlation is anintelligent way of associating a portion of the datasets with anotherportion of the datasets. The data correlation may be based on timesynchronization, shared social relation (e.g., devices are owned by useraccounts in the same social group), shared data dimension (e.g., bothdevices measures weight), shared data source profile (e.g., location ordevice-type, etc.), data owner profile (e.g., user profile or userconfigurations), shared known semantic (e.g., both devices areconsidered “kitchenware”), shared known context (e.g., both devices areoperated in the context of exercising), or any combination thereof.

For example, the data reported from a networked heating system may becorrelated with the data reported from a networked thermometer based ona shared data dimension and a shared data source location. As anotherexample, aggregated data reported from an exercise tracker on a firstuser may be correlated with an aggregated dataset of heart rate data ofthe first user (i.e., because of shared known context of “user health”).The aggregated dataset of heart rate data may in turn then be correlatedwith an aggregated dataset of user reported calorie count from the firstuser (i.e., because of shared known context of “user health”).

The data correlation module 306 is configured to interface with the dataanalysis module 308. The data analysis module 308 is configured todetermine a set of semantic or context data from the correlateddatasets. Note that part of the correlated datasets may include semanticor contextual data already.

Semantic

The data analysis module 308 may determine semantic meaning to each ofthe IoT devices 324 and the data processed by the data correlationmodule 306. Initially, the IoT devices 324 do not have any assignedmeaning to an user. For example, the IoT devices 324 are devices likeswitches, routers, light bulbs, fridge, TV, car, etc. However, to users,the IoT devices 324 symbolize more than just a bulb. Users prefer to usedevices based semantic meanings of the devices. For example, an “X-Box”switch, “my home” router, “kitchen” light, “bathroom” light, “myparents”' fridge, and “kitchen” TV are all potential semantic labelsthat may assist a user when operating a set of IoT devices 324. Thesemantic labels on the IoT devices 324 may also assist the data analysismodule 308 to better understand the context of the user's intentions.For example, a “front door” may be in a context with different defaultbehaviors or interoperable rules than a “bedroom door.” Similarly, datagenerated through these semantically labeled IoT devices 324 may also besemantically labeled. The data analysis module 308 may implementsemantic learning to each user's devices based at least one or more ofthe following methods:

User-based: Semantic meaning may be defined by a user. A user may submitthrough the client application interface 322 or the integrationapplication 328 that a specific device is connected to another device.For example, the user may submit through the client interface that aswitch is connected to a game console X-Box, and hence the switch may belabeled the “X-box switch.”

Device-based: Semantic meaning may be determined via an adaptivecorrelation mechanism as implemented through the data correlation module306 described above. Through network scanning techniques, the adaptivecorrelation mechanism may identify that the user's smartphone sees thespecific light bulb always at the same time when a fridge is alsoidentified. The adaptive correlation mechanism may learn that the lightbulb has significant meaning together with the fridge. Such acorrelation of data enables an understanding that the light bulb is veryclose to the fridge. The data analysis module 306 can then adopt anexisting semantic meaning of the fridge being in the “kitchen” to thespecific light bulb (i.e., labeling the light bulb as the “kitchen”light bulb).

Behavior profile based: The data analysis module 308 may profile user'sbehavioral patterns and identify places, devices and people that a useris connected to during his/her day. For example, when a user isconnected to IoT devices always or highly frequently during working daysfrom a specific router or a geo-location, then the data analysis module308 may label the IoT devices with a semantic meaning of being a “work”device based on association the specific router or with a geolocation ofwork.

Context Awareness

Through semantic awareness and devices' interoperability, the dataanalysis module 308 may recognize the context between and/or amongstdevices, people, places and time. Context may be organized semanticallyfor ease of comprehension by the user of the integration platform.Context may work to approximate or describe a real-life situation,place, event, or people based on datasets collected from the IoT devicesor other nodes (e.g., social network, external user-associated accounts,or geographical databases). Context may work to predict a futuresituation based on datasets collected from the IoT devices. Context mayanswer questions, such as who is doing what activity at when and where,and why is the activity being done. For example, the context of “motheris cooking dinner in the kitchen tonight” may be detected by activationof mother's cellphone near a networked oven that is also activated.Context awareness mechanisms of the data analysis module 308 may bederived through one or more of the following methods:

Behavior profile based: The data analysis module 308 may derive at aparticular context through device behavior analysis. For example,“finished running”, “child arrives home from school” awareness arerecognized based on devices behaviors including when exercise trackerstopped registering movement or when a smart TV is tuned to a cartoonchannel at home in the afternoon.

Socially-based: The data analysis module 308 may derive at a particularcontext through interaction with social graph data from a social networkassociated with a user account. For example, when users communicate totheir friends via a social network, a matching mechanism can be appliedto assign awareness of a friendship relationship to the devices of theuser's friends. A semantic label of a “friend Erica's smart watch” maybe assigned to a wearable device registered to a friend of the usernamed “Erica”. Henceforth, any activity observed through the smart watchmay have a social context to the user as an activity performed by thefriend “Erica”. As another example, when a device registered to “Erica”(e.g., the device is frequently or always with “Erica”) is detected inthe vicinity of the user's active region (e.g., where the data analysismodule 308 has determined the user to be or location of a networked doorof a registered home to the user), the data analysis module 308 mayregister a contextual event of “Erica is at the door.”

Geo-location based: The data analysis module 308 may further identifygeo-locations of multiple devices' activities to determine locationcontext. In each network scanning event of a connected IoT device, ageolocation (longitude, latitude, accuracy) may be collected. Therefore,each IoT device and/or IoT device activity can have a geolocationhistory that defines the device's geolocation context. Geo-location, forexample, may be reported via a global positioning system (GPS) componentor a network module (e.g., via network source triangulation) of the IoTdevices 324.

The data analysis module 308 may also determine the geo-location of anIoT device by generating and maintaining an in-door navigation map.Techniques, such as GPS geo-location and/or cellular network-basedgeo-location navigation systems, may not be as effective or readilyavailable (e.g., due to low signal strength) while in-doors forpositioning people and devices. In one embodiment, an in-doorgeo-location solution may include special hardware systems that areplaced inside the building for indoor geo-location and navigation.

A preferred solution may include geo-location via local networkconnections without additional special hardware systems. In variousembodiments, a directory of connected devices that belong to the usermay be kept in a database store coupled to the data analysis module 308.The directory along with beacon signal from Bluetooth or WiFi may beused to estimate the position of a user indoors. For example, for eachWi-Fi network router scanning, the GPS geolocation and RSSI may both becollected through the data correlation module 306 or the data analysismodule 308. The RSSI enables the data analysis module 308 to positionlocal networking devices (e.g., routers, access points, or switches)relatively to each other in every specific geo-location point. Each IoTdevices connected to the indoor network (e.g., Wi-Fi) through itsvisibility position relative to the local networking devices in everymoment may thus enable locating of the user and IoT device activitiesin-house.

The data analysis module 308 may calculate the set of context inreal-time as data is reported from the IoT devices. Absolute and/orrelative timing of IoT device data may be used for temporal context. Forexample, the data correlation module 306 may correlate IoT deviceactivation times from IoT devices in the same room. The data analysismodule 308 can then compute a user relevant context from the deviceactivation times. For example, if the IoT device activation times areclose to one another within a predetermined time period in the morning,the data analysis module 308 may record a “user has woken up” context.As another example, if IoT device disconnection times for IoT deviceswithin the same room are simultaneous within a very close margin, thedata analysis module 308 may record a “blackout” context. This“blackout” context may be differentiated from when the IoT devicedisconnection times are sequential instead of simultaneous. As yetanother example, a sequential turning off of IoT devices may signal a“user getting ready to sleep” context or “user leaving home” context.

Correlative Insights

Correlative insights may also be determined by the data analysis module308. The correlative insights are actionable insights for facilitatingthe user to make decisions regarding what action(s) to take. FIGS.10A-10B illustrate examples of how the correlative insights may be shownon a consolidated interface generated by the consolidation interfacegenerator module 304. For example, the data correlation module 306 maybe able to collect a first data set of glucose level data from awearable device of a user. The data correlation module 306 may also beable to collect a second data set of activity level data from anotherwearable device of the user. The data correlation module 306 may thencorrelate the two datasets. The data analysis module 308 may determine acorrelative insight that “high activity level leads to sudden drop inglucose level.” This correlative insight may then be used to generateinteroperable rules to notify the user to stop exercising after acertain activity level is reached in order to avoid sudden drops inglucose level.

The consolidation interface generator 304 is configured to provide aconsolidation interface to access and/or manage IoT devices connected tothe integration platform 300. The consolidation interface generator 304enables consolidation of connected devices for a consumer/user to asingle client interface. The single client interface may be presented onthe integration application 328 or any other network device capable ofaccessing the web and connect with the integration backend system 302.The consolidation interface generator 304 may also be configured toprovide access to real-time or non-real-time data captured by one ormore of the IoT devices.

As an specific example, the data correlation module 306 may aggregatedata to generate a “life line” to be presented through the clientinterface generated by the consolidation interface. The “life-line” mayserve as an automated diary related to a user as illustrated in FIG. 4.A users' daily activities and events may all or partially be correlatedtogether in the order of the time of the day.

The “life line” may include context events determined by the dataanalysis module 308 as well as data from the IoT devices. The “lifeline” may be accessible to the user and/or social connections (e.g.,friends in a social network) of the user through the consolidationinterface. The “life line” may provide a big picture view for the userto visualize relevant correlated datasets related to him/herself. The“life line” may tag IoT device data in a timeline automatically based ona context-relevancy determination by the data analysis module 308 orbased on user interaction. The “life line” may also be a motivator forcollection of semantic data from a user. For example, when a user spenta considerable large amount of time in a place, the data correlationmodule 306 can collect and correlate data associated with a default name“place in Menlo park.” The user may then be motivated to correct thesemantic label to “home.”

For example, users may be able to see a life log with places the userhas been, activity tracking data, health statuses, calendar events,weather data—all data correlated together over the timeline of the day.Additionally, the user may be able to add his/her custom events on the“life line”. The “life-line” is thus user specific. The accessibilityand/or configurability of the “life-line” may be secured via privacysettings per user. Data (e.g., measurements, commands, status updates,and etc.) coming from the IoT devices 324 may be aggregated, analyzed,and/or correlated via the data correlation module 306 and/or the dataanalysis module 308. An advantage of the data analysis and datacorrelation is generation of one or more layers of contextual,correlative, and/or semantic insights, trigger events, and/or actions.The data analysis module 308 may apply machine learning on the analyzedand/or correlated data coming from the three layers described above andcreate a sense of “cognition”—understanding of contextual, correlative,and/or semantic events in a user's life. These layers enable predictiveor reflective comprehension of user and/or IoT device behavior patternsand/or trends, and may further enable synthesis of generalizations ofuser and/or IoT device activity or need.

Detection of contextual events may be useful as evident below for thepurposes of generating and executing interoperable IoT device rules,such as for the use by the rule generation module 310, the ruleexecution module 314, and the event track module 316. For example, whenthe friend's (e.g., Erica's) activity tracker is close to the connecteddoor lock of the user, and the user has given permissions, then theconnected door lock can open automatically by recognition of a socialcontext of a “friend at my home next to my connected door.”

Interoperability—Rule Generation and Execution

It has been discovered that interoperability is a significant part ofconsolidation of vertical solutions for IoT devices. Interoperabilityfunctions may be implemented through the rule generation module, thedevice identification module 312, the rule execution module 314, and/orthe event track module 316. Interoperability enables creation of logicalconnections between the connected IoT devices. For example, when a userturns off his office lights and leaves the work, then his hometemperature may be set automatically for a desired temperature. Thelogical connections between the connected IoT devices may be createdthrough users' natural languages. For example, a logical IoT devicesrule may be implemented through a natural language indication by “when Ileave home, turn off my lights and start my Roomba vacuum cleaner” or“when I finish exercising, then cool down my car”.

Once a logical connection is defined, the logical connection can takeplace based on defined interoperable rules without any userinterference. The interoperability functions may be based on atrigger-action mechanism responsible for creating (e.g., the rulegeneration module 310), storing (e.g., the rule generation module 310),validating (e.g., the rule generation module 310), tracking (e.g., eventtracking module 316), and activating (e.g., the rule execution module314) the interoperable rules. For example, the rule generation 310 maycreate, store, and confirm a context event based rule with the user. Theevent track monitor 316 may, for example in substantially real-time orperiodically, recognize context triggered conditions (e.g., “I lefthome”; “finished exercising”, or “Erica entered to the kitchen”). Therule execution module 314 may then execute the context event based rulewhen the context triggered condition is satisfied.

As an example, the rule generation module 310 may have created acontext-based rule of “if user left the house then turn off all in-housedevices.” The event track module 316 through the data analysis module308 may be able to detect in real-time that a “user has left the house”context at time point T₁. Thus in response to the detection of theconditional event at time T₁, the rule execution module 314 may executemultiple shutdown commands to multiple IoT devices at the user's homeaddress.

As another example, an interoperable rule may be an authenticationcondition coupled to unlocking of a connected security device (e.g., anetwork enabled door). The authentication condition, for example, may bea contextual event of “a friend coming over to my house.” The eventtrack module 316 may detect this contextual event when a friend'sactivity tracker device nears the connected door of the user. In thisexample, the geo-location of the connected activity tracker or otherwearable devices of the friend can be used as the basis of confirmingthe authentication condition. The authentication condition may includemultiple other factors, including geo-location proximity of a secondand/or a third device belonging to the user or the user's friend,including smartphones or mobile devices. The rule generation module 310is configured to facilitate creation of rules to control the IoTdevices. The rule generation module 310 may couple to a rule managementinterface. The rule management interface may be part of theconsolidation interface generated by the consolidation interfacegeneration module 304. The rule management interface may be part of theconsolidation interface generated by the consolidation interfacegenerator 304. Rules may be configured by a user or automaticallydetermined by the rule generation module 310. When a rule is determinedby the rule generation module 310, the rule is shown as arecommendation. The recommendation is then suggested through theconsolidation interface for validation by the user. The rules may bestored on the integration backend system 302 or on an IoT deviceassociated with a user who created or validated the rule.

The rules may include conditionals based on events, context, usertrigger, time trigger, or any combination thereof. The rule generationmodule 310 may be coupled a rule management interface that enablesselection(s) of a conditional followed by selection(s) of a command. Arule recommendation may be determined by an adaptive learning mechanism,such as by user behavior pattern (e.g., every morning the user turns onthe air conditioning to 70 degrees), by user-profiling (e.g., otherusers of the same age and/or gender prefer to have a health reportaggregated from health-related IoT devices and therefore a rule togenerate a health report is created), or by social triggers (e.g., afriend who owns a Tesla decides to send over his IoT device rulesassociated with owning a Tesla).

A user is able to define the interoperability between his IoT devicesaccording to his will, preference, habits, and/or desire of automationor any other type of motivation. Based on users' behaviors and users'profiling, the adaptive learning mechanism may recognize user'sbehavioral routine patterns and offer to a user to add an interoperablelogical connection (i.e., IoT interoperable rule) between his/herconnected devices. For example, the data correlation module 306 and thedata analysis module 308 can recognize that every morning a user startsthe coffee machine, turns on the music, leaves a house, and turns offall lights and thermostat. This sequence of IoT device commands to thecoffee machine and the music player may be a recommended rule triggeredby a context event of the user waking. The IoT device commands to thethermostat and the lights may be triggered by the context event of theuser leaving the house. Users may also recommend the interoperablelogical rules to their friends through any communication channel, e.g.,social networks, emails, cellular messages, instant messages, or anycombination thereof.

The event track module 316 is configured to synchronize execution ofrules based on events. When the interoperable IoT rules are generated,execution of the rules may be based on a variety of conditional events.For example, the conditional events may include context-based events,device-state-based events, absolute or relative time-based events,socially triggered events, user profile triggered events, userbehavior/interaction triggered events, or any sequential or parallelcombination thereof. The event track module 316 may interface with thedata analysis module 308 to detect the context-based events and executecontext-based rules generated by the rule generation module 310. Theevent track module 316 may detect conditional events in the connectedIoT devices 324 based on polling the connected IoT devices 324, or basedon interrupts received from the IoT devices 324, the solution specificapplication 322, and/or the integration application 328.

In various embodiments, the event monitoring mechanism of the eventtrack module 316 may be implemented on the integration application 328,the integration backend system 302, the IoT devices 324, or acombination thereof. When some logic of the event track module 316 isimplemented on the IoT devices 324 or the integration application 328,the event track module 316 may be able to detect condition events basedon interrupts instead of polling.

The implementation of the interoperable rule validation, tracking andexecution may be distributed. For example, the implementation of theinteroperable rules may be based on a distributed mesh model incommunication with a central cloud service system (e.g., the integrationbackend system 302). In various embodiments, every user running theintegration application 328 can validate, track, and executeinteroperable rules, even if the interoperable rules do not belong tothe user. For example, if a user lost a Bluetooth tagged keychain, theintegration platform 300 may try to scan proximal environments of otherusers' devices for the user's keychain. Identifiers from the otherusers' devices may remain anonymous and/or encrypted for privacyreasons. The final location of the keychain is also shielded from accessby the other users even if the other users' devices contributed to thelocating of the keychain. An anonymized identifier may then betransferred to the integration backend server 302 to inform the userthat the key chain is found.

The rule execution module 314 is configured to execute aninteroperability logical rule through the IoT devices 324, theintegration backend system 302, the integration application 328, thesolution-specific applications 322, the device-specific backend systems320, or any combination thereof. The rule execution module 314 may beconfigured to communicate with the above systems through thecommunication interface 318 to enable communication between all of theabove devices, applications, and systems. The rule execution module 314may also synchronize execution of commands related to multiple IoTdevices. In various embodiments, similar to tracking of the triggerconditions, the execution of commands may also be distributed, includingvia devices owned by a user who is not the owner of the interoperablerule. In various embodiments, each user who owns devices may enablepermission settings that allow other users to use the owned devices fortracking or execution of interoperable rules.

Blocks, components, and/or modules associated with the IoT integrationplatform system 300 may be implemented as hardware modules, softwaremodules, or any combination thereof. For example, the modules describedcan be software modules implemented as instructions on a tangiblestorage memory capable of being executed by a processor or a controlleron a machine. The tangible storage memory may be a volatile or anon-volatile memory. In some embodiments, the volatile memory may beconsidered “non-transitory” in the sense that it is not a transitorysignal. Software modules may be operable when executed by a processor orother computing device, e.g., a single board chip, a field programmablefield array, a network capable computing device, a virtual machineterminal device, a cloud-based computing terminal device, or anycombination thereof.

Each of the modules may operate individually and independently of othermodules. Some or all of the modules may be executed on the same hostdevice or on separate devices. The separate devices can be coupled via acommunication module to coordinate its operations. Some or all of themodules may be combined as one module.

A single module may also be divided into sub-modules, each sub-moduleperforming separate method step or method steps of the single module. Insome embodiments, the modules can share access to a memory space. Onemodule may access data accessed by or transformed by another module. Themodules may be considered “coupled” to one another if they share aphysical connection or a virtual connection, directly or indirectly,allowing data accessed or modified from one module to be accessed inanother module. In some embodiments, some or all of the modules can beupgraded or modified remotely. The IoT integration platform system 300may include additional, fewer, or different modules for variousapplications.

FIG. 4 is an example of an user interface illustrating an activelifeline diagram 400 for a user account on an integration interface,such as the integration interface 114 of FIG. 1 or the integrationapplication 328 of FIG. 3, consistent with various embodiments of thedisclosed technology. The active lifeline diagram 400, for example, maybe part of the consolidation interface generated by the consolidationinterface generator 304 of FIG. 3. The active lifeline diagram 400 maybe accessible through the integration application 328.

As shown, the active lifeline diagram 400 illustrates a single day of auser, organized by when the user woke up to when the user went to sleep.Although the active lifeline diagram 400 has been illustrated for a timeperiod of a single day, the user may define the length of the historydata to any time period. In various embodiments, the user may scrollback between consecutive time periods along the life diagram 400. Theactive lifeline diagram 400 includes illustrations of semantic labels402, contextual events/activities 404, icons of relevant IoT devices406, relevant IoT device data 408, correlated contextual data 410, andexternally sourced data 412, such as data source from a social networkstatus report.

As a specific example, the correlated contextual data 410 may representa user interface element on the top of the lifeline diagram 400 thatenables tagging of activities or postings specific to fitness activitycontextual labels (e.g., sleep, idle, active, and exercise). As anotherexample, the icon of the relevant IoT device 406 is illustrated alongwith a contextual event that a new device and a new semantic label hasbeen added for “My XBOX.”

The active lifeline diagram 400 may respond and visualize correlativeinsights according to periodic or real-time data, or context updates,enabling users of the active lifeline diagram 400 to take action basedon the insights. The active lifeline diagram 400 may also enable a userto tag or post his/her own updates as part of a life-logging mechanism.An example of a semantic camera interface to facilitate the life loggingis illustrated in FIGS. 11A-11D. For example, the active lifelinediagram 400 may be advantageous in achieving a health action plan withlive feedback information. This is a significant step-up from a merelifestyle blog. The active lifeline diagram 400 may not only consolidatelifestyle and daily routine data (e.g., exercise and performance data)relating to fitness activities, but may also correlate a user'slifestyle habits and daily routine with a context of how healthy theuser is based on numerous measurements and reports.

Elements of the lifeline diagram 400 may be divided into at least thefollowing categories: life logging posts 414, type A elements 416, typeB elements 418, type C elements 420, type D elements 422, type Eelements 424, or any combination thereof. A life log post 414 is a userreported post or tag on the lifeline diagram 400. For example, the lifelogging posts may follow the following rules. A “+” sign may appear atthe top of the lifeline when there is no other activity, on idle time,or when there is no other notification, element, or correlation withinthe same time period. When the plus sign is tapped, the posting optionsmay appear. When choosing the post option, the plus sign may change to acamera icon enabling the semantic camera interface described in FIGS.11A-11D. An editable text may appear on the left of the plus sign, suchas “what is on your mind?” to request the user to specify an activity ora topic, or “what is this place?” to request the user to specify alocation. If the user is idle and does not edit or take a photo for morethan a threshold number of seconds, such as three seconds, then the postbecomes a C type element 420, and the plus sign re-appears.

A type A element 416 is an activity or event where the place anddescription text is a predefined by the integration platform 300.However, a user may edit the text of the type A element 416 (e.g., bytapping once). The edited text may be returned to the integrationplatform 300, such as the integration backend system 302 for futurerendering of the life line diagram 400. A type B element 418 is a nodeon the life line diagram 400. The node image may be defined by theintegration platform 300, where the images may be cached off-line. Thedescription text may be predefined by the server and editable by theuser similar to the type A element 416. Again, the edited text may bereturned to the integration platform 300.

A type C element 420 is a node on the lifeline diagram 400, where thenode is represented by an iconic image. The default iconic image may bereceived from the integration backend system 302. However, the user maytap the iconic image to take and upload a picture to the integrationbackend system 302. A type D element 422 is a node on the lifelinediagram 400, where the node is associated with an activity, a contextualicon, and a display of data. A type E element 424 is a node on thelifeline diagram 400, where the node is a data representation diagram.The user may configure how the data come from the integration backendsystem 302. The user may also configure how the data is represented inthe data representation diagram.

Referring now to FIG. 5, therein is shown a diagrammatic representationof a machine in the example form of a computer system 500 within which aset of instructions, for causing the machine to perform any one or moreof the methodologies or modules discussed herein, may be executed.

In the example of FIG. 5, the computer system 500 includes a processor,memory, non-volatile memory, and an interface device. Various commoncomponents (e.g., cache memory) are omitted for illustrative simplicity.The computer system 500 is intended to illustrate a hardware device onwhich any of the modules or components depicted in the example of FIG. 2or FIG. 3 (and any other components described in this specification) canbe implemented. The computer system 500 can be of any applicable knownor convenient type. The components of the computer system 500 can becoupled together via a bus or through some other known or convenientdevice.

This disclosure contemplates the computer system 500 taking any suitablephysical form. As example and not by way of limitation, computer system500 may be an embedded computer system, a system-on-chip (SOC), asingle-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, or a combination of two or more ofthese. Where appropriate, computer system 500 may include one or morecomputer systems 500; be unitary or distributed; span multiplelocations; span multiple machines; or reside in a cloud, which mayinclude one or more cloud components in one or more networks. Whereappropriate, one or more computer systems 500 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 500 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 500 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

The processor may be, for example, a conventional microprocessor such asan Intel Pentium microprocessor or Motorola power PC microprocessor. Oneof skill in the relevant art will recognize that the terms“machine-readable (storage) medium” or “computer-readable (storage)medium” include any type of device that is accessible by the processor.

The memory is coupled to the processor by, for example, a bus. Thememory can include, by way of example but not limitation, random accessmemory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). Thememory can be local, remote, or distributed.

The bus also couples the processor to the non-volatile memory and driveunit. The non-volatile memory is often a magnetic floppy or hard disk, amagnetic-optical disk, an optical disk, a read-only memory (ROM), suchas a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or anotherform of storage for large amounts of data. Some of this data is oftenwritten, by a direct memory access process, into memory during executionof software in the computer system 500. The non-volatile storage can belocal, remote, or distributed. The non-volatile memory is optionalbecause systems can be created with all applicable data available inmemory. A typical computer system will usually include at least aprocessor, memory, and a device (e.g., a bus) coupling the memory to theprocessor.

Software is typically stored in the non-volatile memory and/or the driveunit. Indeed, for large programs, it may not even be possible to storethe entire program in the memory. Nevertheless, it should be understoodthat for software to run, if necessary, it is moved to a computerreadable location appropriate for processing, and for illustrativepurposes, that location is referred to as the memory in this paper. Evenwhen software is moved to the memory for execution, the processor willtypically make use of hardware registers to store values associated withthe software, and local cache that, ideally, serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient location (from non-volatile storage to hardwareregisters) when the software program is referred to as “implemented in acomputer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

The bus also couples the processor to the network interface device. Theinterface can include one or more of a modem or network interface. Itwill be appreciated that a modem or network interface can be consideredto be part of the computer system 500. The interface can include ananalog modem, isdn modem, cable modem, token ring interface, satellitetransmission interface (e.g., “direct PC”), or other interfaces forcoupling a computer system to other computer systems. The interface caninclude one or more input and/or output devices. The I/O devices caninclude, by way of example but not limitation, a keyboard, a mouse orother pointing device, disk drives, printers, a scanner, and other inputand/or output devices, including a display device. The display devicecan include, by way of example but not limitation, a cathode ray tube(CRT), liquid crystal display (LCD), or some other applicable known orconvenient display device. For simplicity, it is assumed thatcontrollers of any devices not depicted in the example of FIG. 5 residein the interface.

In operation, the computer system 500 can be controlled by operatingsystem software that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile memory and/or drive unit and causes the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or “generating” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments may thus be implemented using a variety of programminglanguages.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies ormodules of the presently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processing units or processors in acomputer, cause the computer to perform operations to execute elementsinvolving the various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include but are not limitedto recordable type media such as volatile and non-volatile memorydevices, floppy and other removable disks, hard disk drives, opticaldisks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks, (DVDs), etc.), among others, and transmission type media such asdigital and analog communication links.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change or transformation inmagnetic orientation or a physical change or transformation in molecularstructure, such as from crystalline to amorphous or vice versa. Theforegoing is not intended to be an exhaustive list of all examples inwhich a change in state for a binary one to a binary zero or vice-versain a memory device may comprise a transformation, such as a physicaltransformation. Rather, the foregoing are intended as illustrativeexamples.

A storage medium typically may be non-transitory or comprise anon-transitory device. In this context, a non-transitory storage mediummay include a device that is tangible, meaning that the device has aconcrete physical form, although the device may change its physicalstate. Thus, for example, non-transitory refers to a device remainingtangible despite this change in state.

FIG. 6 shows an embodiment of a wireless device 600 which includes thecapability for wireless communication. The wireless device 600 may beincluded in any one of the devices shown in FIG. 2, although alternativeembodiments of those devices may include more or fewer components thanthe wireless device 600.

Wireless device 600 may include an antenna system 401. Wireless device600 may also include a digital and/or analog radio frequency (RF)transceiver 602, coupled to the antenna system 601, to transmit and/orreceive voice, digital data and/or media signals through antenna system601.

Wireless device 600 may also include a digital processing system 603 tocontrol the digital RF transceiver and to manage the voice, digital dataand/or media signals. Digital processing system 603 may be a generalpurpose processing device, such as a microprocessor or controller forexample. Digital processing system 603 may also be a special purposeprocessing device, such as an ASIC (application specific integratedcircuit), FPGA (field-programmable gate array) or DSP (digital signalprocessor). Digital processing system 603 may also include otherdevices, as are known in the art, to interface with other components ofwireless device 600. For example, digital processing system 603 mayinclude analog-to-digital and digital-to-analog converters to interfacewith other components of wireless device 600. Digital processing system603 may include a media processing system 609, which may also include ageneral purpose or special purpose processing device to manage media,such as files of audio data.

Wireless device 600 may also include a storage device 604, coupled tothe digital processing system, to store data and/or operating programsfor the wireless device 600. Storage device 604 may be, for example, anytype of solid-state or magnetic memory device.

Wireless device 600 may also include one or more input devices 605,coupled to the digital processing system 603, to accept user inputs(e.g., telephone numbers, names, addresses, media selections, etc.)Input device 605 may be, for example, one or more of a keypad, atouchpad, a touch screen, a pointing device in combination with adisplay device or similar input device.

Wireless device 600 may also include at least one display device 606,coupled to the digital processing system 603, to display informationsuch as messages, telephone call information, contact information,pictures, movies and/or titles or other indicators of media beingselected via the input device 605. Display device 606 may be, forexample, an LCD display device. In one embodiment, display device 606and input device 605 may be integrated together in the same device(e.g., a touch screen LCD such as a multi-touch input panel which isintegrated with a display device, such as an LCD display device). Thedisplay device 606 may include a backlight 606A to illuminate thedisplay device 606 under certain circumstances. It will be appreciatedthat the Wireless device 600 may include multiple displays.

Wireless device 600 may also include a battery 607 to supply operatingpower to components of the system including digital RF transceiver 602,digital processing system 603, storage device 604, input device 605,microphone 605A, audio transducer 608, media processing system 609,sensor(s) 610, and display device 606. Battery 607 may be, for example,a rechargeable or non-rechargeable lithium or nickel metal hydridebattery. Wireless device 600 may also include audio transducers 608,which may include one or more speakers, and at least one microphone605A. In certain embodiments of the present disclosure, the wirelessdevice 600 can be used to implement at least some of the methodsdiscussed in the present disclosure.

FIG. 7 is a flow diagram of a method 700 of data consolidation,consistent with various embodiments of the disclosed technology. Themethod 700 includes extracting a first data record (e.g., a data entry,measurement, stream, etc.) from a data source connected to an IoTintegration platform, such as the IoT integration platform 200 of FIG. 2or the integration platform 300 of FIG. 3, at a step 702. The datasource may be an external data source, data reported from one or moreIoT devices, or analyzed and/or correlated data based on the above, suchas contextual data, correlations, semantic data, or other metadata. Thestep 702 may include determining an unique identifier of a first IoTdevice, where the first IoT device is the data source. The step 702 maybe performed by the data correlation module 306 of FIG. 3.

Optionally data records may be normalized. For example, the first datarecord may be normalized with respect to other data record along a samedata dimension at step 704. The step 704 may be performed by the datacorrelation module 306 as well.

Part of the data consolidation also includes correlating the datarecords and analyzing the data records to determine semantic label,context, and/or relevancy. For example, the data correlation module 306may correlate the first data record with a second data record into adata set at a step 708. The data correlation may be based on a shareddata dimension, a shared data context, a shared data source, a sharedrelevancy topic, a shared data semantic label, or any combinationthereof. Optionally, the step 704 and the step 706 may be skipped priorto the data correlation.

The data analysis module 708 may analyze the first data record togenerate a derivative record relevant to a user context at a step 708.The data analysis at the step 708 may be performed on the correlateddata records in the data set generated at the step 706. Alternatively,the data analysis may also be performed directly on the extracted firstdata record and/or the normalized first data record of the steps 702 and704, respectively.

The derivative record may include determination of a context of an IoTdevice activity. The derivative record may include determination of asemantic label for an IoT device associated with the first data record.The derivative record may include other predictive, trending, and/orcomparative analytics. The derivative record may be used in anotherinstance of the data consolidation flow as the extracted data record,such as in the step 702. The derivative record may be formatted as anatural language statement in any number of languages, such as theuser's natural language.

After the above steps, different data records and data sets may beaggregated into a data cluster for a particular contextual relevantgrouping, such as aggregating the first data record into a data clusterat step 710. The derivative record may also be aggregated into the datacluster. The data cluster may represent an aggregation of data that isbeyond correlated. For example, a heart rate monitor data record may becorrelated at the step 706 with a glucose level monitor data recordbecause of a shared semantic and context of health related data.However, these health related data may be aggregated into a data clusterfor other non-health-related activities on the same day because therelevant grouping of the data cluster pertains to the activities of theday.

After the above steps of data processing, the derivative record and/orthe first data record is presented (e.g., visualized or audiblypresented) on an integration interface for the user at a step 712. Thefirst data record and/or the derivative record may be presented on theintegration interface along with other data sets or data records of thedata cluster. The context indication may be determined based on thederivative record from the step 714. The visualization may also includecomparative visualization, semantic visualization, or topicalvisualization (e.g., based on topical relevancy). The visualization maypresent the first data record within the aggregated data cluster. Thevisualization may present the first data record after the first datarecord is normalized. The visualization may present the first datarecord and the correlated second data record simultaneously together forillustrating the shared data category or the shared context, such as thecontext from the context indication.

FIG. 8 is a flow diagram of a method 800 of interoperable IoT rulemanagement, consistent with various embodiments of the disclosedtechnology. The method 800 includes receiving a selection of a semanticlabel associated with an IoT device from a user at step 802. The step802 may be implemented by the rule generation module 310 of FIG. 3through correspondence with a rule management interface. The semanticlabel may be generated from the data analysis module 308 of FIG. 3.Several semantic labels may be presented on the rule managementinterface, such as via the integration application 328 of FIG. 3 or therule management interface illustrated in FIGS. 9A-9C. In variousembodiments, the semantic label may be associated with more than one IoTdevices.

The rule generation module 310 may determine a recommendation of aninteroperable rule based on the semantic label in a step 804. Eachinteroperable rule may include a contextual condition trigger and anaction policy to be executed when the contextual condition trigger isdetected. The recommendation of the interoperable rule may be determinedbased on the selected semantic label, available context, and/or datadimensions from the IoT device associated with the semantic label. Theinteroperable rule recommendation may be determined based on a historyof user behavior in operating IoT devices. The interoperable rulerecommendation may be based on a previously configured interoperablerule by the user. The interoperable rule recommendation may bedetermined based on previously configured interoperable rules by otherusers of a similar user profile (e.g., age, gender, hobby, profession,or other demographic profile) as the user. The interoperable rulerecommendation may be determined based on socially recommendedinteroperable rules by another user who is associated with the user viaa social connection.

In response to receiving the selection, rule generation module 310 maypresent the recommendation of the interoperable rule on the rulemanagement interface in a step 806. At a step 808, the rule managementinterface may receive a confirmation from the user to activate theinteroperable rule.

In response to activation of the interoperable rule, the event trackmodule 216 may monitor to detect the condition trigger of theinteroperable rule in a network of connected IoT devices in a step 810.The condition trigger may include a contextual determination, a datapattern, or a state of the IoT device associated with the selectedsemantic label. When the condition trigger is detected, the ruleexecution module 314 may execute the action policy of the interoperablerule to control one or more of the connected IoT devices in a step 812.The one or more of the connected IoT devices to be controlled by theaction policy may include the IoT device associated with the selectedsemantic label.

FIG. 9A is a screen shot of a rule management interface 900 of anintegration platform at a rule activation stage, consistent with variousembodiments of the disclosed technology. FIG. 9A illustrates a list ofinteroperable rules 902, each with a condition trigger 904 linked to anaction policy 906. Each condition trigger 904 may be described withrespect to the semantic label and context of the IoT device involved forthe condition trigger 904. A monitor device icon 908 may represent theIoT device involved with the condition trigger 904. Similarly, eachaction policy 906 may be described with respect to the semantic labeland context of the IoT device involved for the action policy 906. Anaction device icon 910 may represent the IoT device involved with theaction policy 906.

FIG. 9B is a screen shot of the rule management interface 900 of theintegration platform at a condition selection stage, consistent withvarious embodiments of the disclosed technology. FIG. 9B illustrates acircle of various condition triggers 904 around the monitor device icon908 for a user to select. FIG. 9C is a screenshot of the rule managementinterface 900 of the integration platform at an action selection stage,consistent with various embodiments of the disclosed technology. FIG. 9Cillustrates a circle of various action policies 906 around the actiondevice icon 910 for the user to complete the interoperable rule 902.

FIG. 10A is an exemplary screen shot 1000 illustrating the consolidationinterface showing a correlative insight, consistent with variousembodiments of the disclosed technology. FIG. 10A illustrates acorrelative insight 1002 determined based on analysis of correlated dataregarding a user's activity and glucose level as shown in the correlateddata diagram 1004. FIG. 10A further illustrates an interoperable rule1006 that may be defined and/or configured by the user in response toviewing the correlative insight 1002.

FIG. 10B is an exemplary screen shot 1050 illustrating the consolidationinterface generating the interoperable rule 1006 from the correlativeinsight, consistent with various embodiments of the disclosedtechnology. The interoperable rule 1006 of FIG. 10A may be furtherdescribed and/or configured. For example, the screen shot 1050illustrates a description of a recommended interoperable rule 1006 ofalerting the user when there is high activity range reached.

FIG. 11A is an exemplary screen shot illustrating a semantic camerainterface 1100 at a first stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology. At the first stage, the user isrequested to add a node to the life line diagram. A semantic camera icon1102 is shown in the center of the semantic camera interface 1100 totake a picture of node in question for labeling and/or imagerecognition.

FIG. 11B is an exemplary screen shot illustrating the semantic camerainterface 1100 at a second stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology. At the second stage, the useris asked to semantically label a type of entity of the node added. Thebottom of the screen shot shows the recognized entity 1104 associatedwith the node to be added as determined by the integration platform 300.The center of the screen shot shows an entity type query 1106 thatrequests the user to label the type of the node added.

FIG. 11C is an exemplary screen shot illustrating a semantic camerainterface 1100 at a third stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology. At the third stage, the user isasked to semantically label the node added. The center of the screenshot shows a semantic label query 1108 that requests the user tosemantically label the node added.

FIG. 11D is an exemplary screen shot illustrating a semantic camerainterface 1100 at a fourth stage, the semantic camera interface used inconjunction with a life line diagram, consistent with variousembodiments of the disclosed technology. At the fourth stage, the useris asked to associate the node added with one or more people. The centerof the screen shot shows a social association query 1110 that requeststhe user to associate the node added with one or more people.

1-20. (canceled)
 21. A method of deriving semantic meaning of Internetof Things (loT) devices connected via an loT integration platform,comprising: collecting device data from a plurality of loT devicesconnected via the loT integration platform; determining a set offeatures by analyzing corresponding time series of events among any ofthe plurality of loT devices and generating one or more clusterscorrelating the device data using natural language processing;performing behavioral analysis to identify one or more user behaviors,wherein the behavioral analysis comprises incrementally populating aprofile with device data associated with either of a particular loTdevice or a particular user, and applying a model to the profile toidentify behavioral patterns and behavior changes; and determining, byutilizing a probabilistic measure, a semantic meaning associated withany device among the plurality of loT devices based on any of the set offeatures and the identified one or more behaviors.
 22. The method ofclaim 21, wherein the device data includes geolocation data, broadcastdata, device protocol, data received by an loT device, data transmittedby an loT device, data services information, or any combination thereof.23. The method of claim 21, further comprising: collecting public datafrom a public library and/or a public device; mapping the public data tothe collected device data; and determining another set of features basedon identified correlation based on the mapping.
 24. The method of claim23, wherein determining the semantic meaning is further based on thedetermined another set of features.
 25. The method of claim 21, whereindetermining the set of features is performed using a machine learningmodel, a statistical model, a rules model, a scoring model, incrementalcalculations, or any combination thereof.
 26. The method of claim 21,wherein determining the set of features is performed by applying anadaptive correlation mechanism to one or more previously generated datamodels to generate an aggregate data model.
 27. The method of claim 21,wherein determining the set of features further comprises: identifyingan event trigger resulting from analyzing the corresponding time seriesof events.
 28. The method of claim 21, wherein determining the set offeatures further comprises: labeling any of the one or more clusterswith a label corresponding to a meaning identified in the device dataassociated with a particular cluster.
 29. The method of claim 28,wherein determining the semantic meaning is further based on the labelcorresponding to the meaning identified in the device data associatedwith a particular cluster.
 30. The method of claim 21, furthercomprising: generating, based on the determined semantic meaning, aninteroperable rule that includes a condition trigger to be detected byat least a first loT device and an action policy to be executed by atleast a second loT device at satisfaction of the condition trigger,wherein the loT device associated with the determined semantic meaningis the first loT device for detecting the condition trigger, the secondloT device for executing the action policy, or both.
 31. The method ofclaim 30, further comprising: upon detecting the condition trigger,executing the action policy on the second loT device, wherein the actionpolicy includes inducing a label.
 32. A method of deriving semanticmeaning of Internet of Things (loT) devices connected via an loTintegration platform, comprising: monitoring device data from aplurality of loT devices connected via the loT integration platform;determining a set of features by analyzing corresponding time series ofevents among any of the plurality of loT devices by applying an adaptivecorrelation mechanism to one or more previously generated data models togenerate an aggregate data model, and generating one or more clusterscorrelating the device data using natural language processing;performing behavioral analysis to identify one or more user behaviors;and determining, by utilizing a probabilistic measure, a semanticmeaning associated with any device among the plurality of loT devicesbased on any of the set of features and the identified one or morebehaviors.
 33. The method of claim 32, wherein the behavioral analysiscomprises incrementally populating a profile with device data associatedwith either of a particular loT device or a particular user, andapplying a model to the profile to identify behavioral patterns andbehavior changes.
 34. The method of claim 32, further comprising:collecting public data from a public library and/or a public device;mapping the public data to the collected device data; and determininganother set of features based on identified correlation based on themapping.
 35. The method of claim 32, wherein determining the set offeatures further comprises: labeling any of the one or more clusterswith a label corresponding to a meaning identified in the device dataassociated with a particular cluster, wherein determining the semanticmeaning is further based on the label corresponding to the meaningidentified in the device data associated with a particular cluster. 36.A machine-implemented integration platform system for deriving semanticmeaning of Internet of Things (loT) devices connected thereto, theintegration platform system comprising: a communication interface havingprotocols to communicate with the plurality of IOT devices; one or moreprocessors connected to the communication interface, wherein the one ormore processors are configured to: collect device data, via thecommunication interface, from a plurality of loT devices; determine aset of features by analyzing corresponding time series of events amongany of the plurality of loT devices and generating one or more clusterscorrelating the device data using natural language processing; performbehavioral analysis to identify one or more user behaviors, wherein thebehavioral analysis comprises incrementally populating a profile withdevice data associated with either of a particular loT device or aparticular user, and applying a model to the profile to identifybehavioral patterns and behavior changes; and determine, by utilizing aprobabilistic measure, a semantic meaning associated with any deviceamong the plurality of loT devices based on any of the set of featuresand the identified one or more behaviors.
 37. The system of claim 36,wherein the one or more processors are further configured to: labelingany of the one or more clusters with a label corresponding to a meaningidentified in the device data associated with a particular cluster,wherein determining the semantic meaning is further based on the labelcorresponding to the meaning identified in the device data associatedwith a particular cluster.
 38. The system of claim 36, wherein the oneor more processors are further configured to: identify an event triggerresulting from analyzing the corresponding time series of events. 39.The system of claim 36, further comprising: an event track moduleconfigured to detect an event trigger among the plurality of loT devicesconnected to the loT integration platform by monitoring data collectedfrom one or more sensors of the plurality of loT devices.
 40. The systemof claim 39, further comprising: a rule execution module, coupled to theevent track module to receive an indication that the event trigger isdetected, configured to execute an action policy by controlling one ormore of the plurality of loT devices when the event trigger is detectedby the event track module.